When teams start working with data, the earliest mistake they make is failing to identify the right market problem or align the problem they are trying to solve with the broader organizational strategy.
A truly effective data analytics project requires more than just technical skills and the right data models. With the non-technical elements of a successful data analytics project outlined below, your data team can uncover powerful insights that often go untapped.
Here are 4 elements of successful data projects.
#1 Deliberate Dialogue
It’s important to have the right context for your analytics project. And that’s true whether you are the data team or the product team. Whether you are on a data team or product team, all parties should commit to being open and listening actively.
It’s easy to quickly jump to conclusions and say, “We have analytics and insights.” However, moving too fast might mean failing to address biases that cause us to look at the data in a specific way.
Instead, we should carefully examine if any cognitive biases are impacting our insights and actively listen to our stakeholders.
Most of us have debates in our organizations; debates aren’t about listening. The goal of a debate is to get your point across no matter what. While someone else is talking, you might be thinking about how you’re going to contradict their point to prove your own. So, deliberate dialogue versus debate is a big step in the right direction.
#2 Managing Uncertainty
We live in a volatile, complex, ambiguous, and uncertain world. That means our brains are navigating uncertainty. You need to educate leaders that data always has an element of uncertainty.
We could run a model about the segmentation of our customers and whether they’ll buy our product. The output might indicate a 95% confidence interval, which isn’t certain. It’s best if all the decision-makers understand that everything is a probability.
There is no situation where we can say there is a 100% chance that investing in a specific demographic will increase our revenue.
You should acknowledge that you will never have all the data, and you will never have all the answers. We don’t have any data on what will happen in the future. It’s not there.
So, we have to make predictions using foresight and strategy.
And, if you’re data literate, you’re more likely to know if your strategy is a good bet.
Simply put, we manage uncertainty in data projects by seeing our efforts as reducing risk with data-driven insights.
#3 Knowing the Audience
Understanding your audience, and tailoring your communication to fit their needs and knowledge, is an essential communication tactic for any data analytics project. Suppose you’re an advanced analyst and do some complex modeling using advanced visualization (like a box plot). Your audience may not be familiar with that level of advanced analytics and data visualization – this risks them misunderstanding your data, or tuning out completely.
Studies show people listen and attach emotionally to stories instead of just numbers. The first thing is to pick the proper visualization and pick the correct terminology to map to your stakeholders. You must meet them at their level and communicate the stakes of the data problem at hand.
Essentially, your job is to answer the question, “So what?” If you’re sharing numbers from the latest marketing campaign with the goal of increasing the marketing budget, the approach will be different from communicating with the creative team.
It comes down to knowing the stakeholders, not simplifying, but prioritizing what you’re going to show them. Everything else is just noise.
#4 Systems Thinking
Which numbers matter? That question is hard to answer because it’s not a simple one-to-one ratio. You can’t say, “This number drives this number,” and you’re done. Twenty other numbers are impacting an outcome. If you’re lucky enough to have a data scientist, some algorithms help you understand which numbers have a higher correlation.
However, if you don’t, one of the non-technical elements of a successful data project you can employ is systems thinking. The goal of systems thinking is to visualize a system map, which visualizes whether those variables are correlated (and if they are, how strongly). System maps don’t share discrete numbers – they just visualize the relationship between variables. If you communicate those relationships well, you’ll have a dialogue with the stakeholders.
Here’s an example of systems thinking:
In a business context, let’s say sales revenue is down. So, you use active listening skills to uncover hypotheses from stakeholders as to why that might be the trend we’re seeing.
Here are some possible variables that would impact sales:
- Discounting prices
- Fewer sales reps in the past
- Charging more
- Supply chain delays
Then, analyze the data. If you notice you’re selling more units, but we’re discounting 50% more, you can say, “We have the demand, we have the capacity, but we just need to change our discount strategy.”
You may have hundreds of variables at play, but you can highlight the most impactful first. Use a simple model and slowly add in complexity.
A Word on Data Culture
One final factor that influences the success of data analytics projects is a company culture that supports data efforts. This environment is more likely to incorporate valuable insights rather than ignore them.
But how do you influence a data culture without being the CEO or in leadership? Start by picking a strategic data project that will positively impact the company if it succeeds, but will not have a significant negative impact if it fails.
When possible, it helps to start data projects at the beginning of the pipeline, which is:
- Raw data
- Data lineage
- Data management
- Data governance
- Data at integration
When you complete it, share the information with the stakeholders and leaders to demonstrate the value of the data analytics project.
